iode.Equation.estimate

Equation.estimate(self, from_period: str | Period = None, to_period: str | Period = None)

Estimate the present equation.

At the end of the estimation process, certain variables and scalars are automatically created if the process has converged. These variables and scalars can be used for computational purposes and, as they are part of the global workspace, can be saved for future use.

The tests resulting from the last estimation are saved as scalars. The same applies to residuals, left-hand and right-hand members of equations.

Saved tests (as scalars) have the following names:

  • e0_n : number of sample periods

  • e0_k : number of estimated coefficients

  • e0_stdev : std dev of residuals

  • e0_meany : mean of Y

  • e0_ssres : sum of squares of residuals

  • e0_stderr : std error

  • e0_stderrp : std error percent (in %)

  • e0_fstat : F-Stat

  • e0_r2 : R square

  • e0_r2adj : adjusted R-squared

  • e0_dw : Durbin-Watson

  • e0_loglik : Log Likelihood

Calculated series are saved in special variables:

  • _YCALC0 : right-hand side of the equation

  • _YOBS0 : left-hand side of the equation

  • _YRES0 : residuals of the equation

Outside the estimation sample, the series values are \(NA\).

Parameters:
from_periodstr or Period, optional

The starting period of the execution range. Defaults to the starting period of the current Variables sample.

to_periodstr or Period, optional

The ending period of the execution range. Defaults to the ending period of the current Variables sample.

Warning

If the present equation belongs to a block, you must use the Equations.estimate() method instead.

Examples

>>> from iode import SAMPLE_DATA_DIR, equations, scalars, variables
>>> equations.load(f"{SAMPLE_DATA_DIR}/fun.eqs")
>>> variables.load(f"{SAMPLE_DATA_DIR}/fun.var")

>>> eq_ACAF = equations["ACAF"]
>>> eq_ACAF.lec
'(ACAF/VAF[-1]) :=acaf1+acaf2*GOSF[-1]+\nacaf4*(TIME=1995)'

>>> # create scalars
>>> scalars["acaf1"] = 0., 1.
>>> scalars["acaf2"] = 0., 1.
>>> scalars["acaf4"] = 0., 1.

>>> # estimate the ACQF equation
>>> eq_ACAF.estimate("1980Y1", "1996Y1")
>>> scalars["acaf1"]
Scalar(0.0157705, 1, 0.00136949)
>>> scalars["acaf2"]
Scalar(-7.96505e-06, 1, 1.48247e-06)
>>> scalars["acaf4"]
Scalar(-0.0085027, 1, 0.00208257)